Rs. Harris et al., An objective analysis of the pressure-volume curve in the acute respiratory distress syndrome, AM J R CRIT, 161(2), 2000, pp. 432-439
To assess the interobserver and intraobserver variability in the clinical e
valuation of the quasi-static pressure-volume (P-V) curve, we analyzed 24 s
ets of inflation and deflation P-V curves obtained from patients with ARDS.
We used a recently described sigmoidal equation to curve-fit the P-V data
sets and objectively define the point of maximum compliance increase of the
inflation limb (P-mci,P-i) and the true inflection point of the deflation
limb (P-inf,P-d). These points were compared with graphic determinations of
lower Pflex by seven clinicians. The graphic and curve-fitting methods wer
e, also compared for their ability to reproduce the same parameter value in
data sets with reduced number of data points. The sigmoidal equation fit t
he P-V data with great accuracy (R-2 = 0.9992). The average of Pflex determ
inations was found to be correlated with P-mci,P-i (R = 0.89) and P-inf,P-d
(R = 0.76). Individual determinations of Pflex were less correlated with t
he corresponding objective parameters (R = 0.67 and 0.62 respectively). Pfl
ex + 2 cm H2O was a more accurate estimator of P-inf,P-d (2 SD = +/-6.05 cm
H2O) than Pflex was of P-mci,P-i (2 SD = +/-8.02 cm H2O). There was signif
icant interobserver variability in Pflex, with a maximum difference of 11 c
m H2O for the same patient (SD = 1.9 cm H2O). Clinicians had difficulty rep
roducing Pflex in smaller data sets with differences as great as 17 cm H2O
(SD = 2.8 cm H2O). In contrast, the curve-fitting method reproduced P-mci,P
-i with great accuracy in reduced data sets (maximum difference of 1.5 cm H
2O and SD = 0.3 cm H2O). We conclude that Pflex rarely coincided with the p
oint of maximum compliance increase, defined by a sigmoid curve-fit with la
rge differences in Pflex seen both among and within observers. Calculating
objective parameters such as P-mci,P-i or P-inf,P-d from curve-fitted P-V d
ata can minimize this large variability.